Implementation of Model Predictive Control in Industrial Gasoline Desulfurization Process

Abstract

Abstract Sulfur is an important pollutant that can severely prevent an implementation of all major pollution control strategies. Thus, to reduce air pollution and to comply with strict environmental regulations, sulfur content in all types of fuel produced is required to be lowered to a certain level. A selective desulfurization process is used to reduce sulfur content of fluidized catalytic cracked (FCC) naphtha, which is a blending component for gasoline product. Though, the desulfurization process can considerably lower sulfur content of the naphtha. Some undesirable olefin saturation reactions are also occurred, resulting in octane loss of the gasoline product. The octane loss depressingly influences economic performances of the plant. Thus, optimizing the operation in order to minimize the octane loss while still complying with sulfur specification and other process constraints is necessary. The operation optimization can be accomplished by implementing model predictive control (MPC). In this work, we focus on the implementation of MPC in the selective desulfurization process in order to strictly control sulfur content in the gasoline product while minimizing octane loss. A soft-sensor for on-line estimating sulfur content in gasoline product was designed and implemented. A series of step tests were performed to build empirical dynamic models. The models obtained were validated and used in MPC design. Analysis of benefit was performed with data collected before and after MPC implementation. The results showed that after MPC implementation, the control performances were improved by shifting mean of sulfur content in product close to the high limit operation. Thus, energy consumption was significantly decreased.